aiqbench analysis
This research extends the Cooperative Multi-Task Semantic Communication framework to jointly handle heterogeneous classification and regression tasks using the information maximization principle on complex perception datasets. By optimizing semantic encoders over bit-accurate reconstruction, the study demonstrates significant performance gains for future intelligent networks requiring simultaneous execution of diverse AI functions. The findings offer critical design insights regarding common unit capacity when integrating mixed discrete and continuous variables in next-generation communication systems.
Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks
arXiv:2609.03977v1 Announce Type: cross Abstract: Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accurate reconstruction in future intelligent networks. In our prior work [1], we introduced the cooperative multi-task SemCom (CMT-SemCom) framework, in which the semantic encoder is divided into a common unit (CU) and multiple specific units (SUs) to facilitate cooperative multi-task processing. However, CMT-SemCom has been evaluated on homogeneous classification tasks on simplistic datasets, limiting its applicability to real-world perception systems. In this paper, we extend our CMT-SemCom to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset. We adopt the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables. In particular, we benchmark the proposed framework against independent single-task training, a conventional task-agnostic digital transmission, and single-encoder multi-decoder SemCom. Additionally, we investigate the impact of CU capacity on joint task performance, providing design insights. Extensive evaluations demonstrate that CMT-SemCom significantly outperforms the benchmarks.
Abstract:Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accurate reconstruction in future intelligent networks. In our prior work [1], we introduced the cooperative multi-task SemCom (CMT-SemCom) framework, in which the semantic encoder is divided into a common unit (CU) and multiple specific units (SUs) to facilitate cooperative multi-task processing. However, CMT-SemCom has been evaluated on homogeneous classification tasks on simplistic datasets, limiting its applicability to real-world perception systems. In this paper, we extend our CMT-SemCom to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset. We adopt the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables. In particular, we benchmark the proposed framework against independent single-task training, a conventional task-agnostic digital transmission, and single-encoder multi-decoder SemCom. Additionally, we investigate the impact of CU capacity on joint task performance, providing design insights. Extensive evaluations demonstrate that CMT-SemCom significantly outperforms the benchmarks.
Signal Processing (eess.SP); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
arXiv:2609.03977 [eess.SP]
(or arXiv:2609.03977v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2609.03977 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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